Search Results (735)

Understanding genotype × environment interactions (G × E) is essential for selection of stable and productive clones in poplar breeding. However, the performance of hybrid poplar clones often varies unpredictably across sites, hindering efficient regional deployment. In this study, we evaluated the growth performance of 21 hybrid Populus section Aigeiros clones under five contrasting environmental conditions (YI, SHAN, SHEN, FEI, and JUAN). At the end of the growing season, we measured tree height (H), diameter at breast height (DBH), and individual tree volume (V). Both parametric and non-parametric stability statistics were used to assess individual V and fitted a mixed linear model in ASReml–R to generate best linear unbiased prediction (BLUP) values for subsequent genotype plus genotype-by-environment (GGE) biplot analysis. All measured traits exhibited substantial phenotypic variation, with coefficients of variation ranging from 17% to 43%. Genotype, environment, and G × E interaction effects were all highly significant (p < 0.01). The GGE biplot explained 89.88% of the total variation in individual V across test environments. Clones 1617 and 1618 exhibited high productivity, broad stability and strong adaptability across sites. Site discrimination and representativeness analyses revealed that SHAN, SHEN, FEI and JUAN were more informative, whereas YI showed weaker genotype differentiation. Clones 1617 and 1618 were particularly well adapted to FEI and JUAN, while clones 81, 1618, 13–22, 1607, I–107 and 1617 performed favourably in the other mega-environment. Our results indicate that integrating BLUP with GGE biplot analysis provides a robust framework for dissecting G × E and guiding clone selection in multi-environment poplar trials. The study provides valuable insights for refining poplar deployment strategies under diverse environmental conditions. Full article

Background/Objectives: Current trends show a significant increase in Alzheimer’s disease and related dementias (ADRD) rates, reflecting a growing need for interventions. Recently, non-pharmacological interventions have received increased recognition as a viable option for ADRD prevention and disease modification. The current study focuses on a music-based community engagement program, titled B Sharp, to investigate the effects of music experiences on domain- and subtest-specific cognitive processing in ADRD. Methods: Participants were provided season tickets (six performances over ten months) to a local MasterWorks Symphony. Across the program’s three seasons (18 individual musical performances), a total of 55 individuals with ADRD participated. Subtest- and domain-specific changes in cognition were assessed before and after each individual symphony performance of 1.5–3 h duration. Variables of interest include auditory-verbal (N = 67) and visuospatial (N = 49) cognitive subtests. Results: In a comparison of pre- and post-symphony performance scores, results indicate statistically significant improvements on a visuospatial working memory task (Coding) (p = 0.021) and statistically significant decline on verbal working memory/immediate memory (List Learning/Story Memory) (p = 0.045). Significant differences were not observed on the remaining subtests. Discussion: Results suggest that even brief exposure to music results in cognitive benefits on visuospatial working memory and contribute to a body of findings which suggest that non-pharmacological arts-based programs may be beneficial for adults with ADRD for specific types of cognition, even for single or sporadic attendance. As research in arts programming increases, it is hoped that findings will guide how to apply various types of programs for specific cognitive deficits accompanying ADRD. Full article

The production of “winter wines” in south-eastern Brazil, enabled by the double pruning technique, requires precise assessment of grape technological maturity to ensure high-quality outputs. However, conventional monitoring approaches are destructive, labor-intensive, and limited in their ability to capture vineyard spatial variability. This study aimed to develop and validate a non-destructive predictive framework for Soluble Solids (°Brix) and Titratable Acidity (TA) by integrating spatial remote sensing data with temporal agrometeorological information. Multispectral imagery was acquired via an unmanned aerial vehicle in a vineyard cultivated with Sauvignon Blanc and Syrah, from which vegetation indices were derived and combined with Growing Degree-Days to train machine learning models, including Random Forest, Multilayer Perceptron, and XGBoost. The incorporation of agrometeorological data significantly improved predictive performance compared to models based solely on vegetation indices. Among the tested algorithms, XGBoost achieved the highest accuracy, with coefficients of determination of 0.89 for °Brix and 0.77 for TA, achieved by XGBoost on an independent hold-out test set. Model interpretability analysis indicated that Growing Degree-Days and cultivar were the primary drivers of maturation dynamics, while vegetation indices refined predictions by accounting for spatial variability in plant vigor. Overall, the proposed approach represents a promising proof-of-concept framework for non-destructive maturity monitoring in precision viticulture, supporting improved monitoring of grape maturation. However, multi-season validation across diverse vineyard conditions is required to confirm its generalizability and support its application as a routine decision-support tool. Full article

Accurate monitoring of inundation in rice paddies is essential for optimizing water use efficiency and mitigating methane emissions; yet, detecting water beneath dense rice canopies remains a major challenge. This study proposed a reliable classification approach applied to the Winter–Spring 2025 season in An Giang province, Vietnam, by integrating multi-temporal ALOS-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) SAR data with in situ field surveys. Time-series Sentinel-1 observations were used to estimate rice phenology (rice age), while multi-polarization backscatter from ALOS-2 PALSAR-2 was analyzed to discriminate inundated from non-inundated conditions across different growth stages. Results demonstrated that L-band signals, particularly in VV polarization, penetrated dense vegetation effectively, enabling classification of inundated vs. non-inundated fields with an overall accuracy of 81% and a Kappa coefficient of 0.77. The resulting multi-date inundation maps revealed distinct flooding regimes consistent with local field survey observations. These findings demonstrated the potential of L-band VV SAR data for characterizing sub-canopy inundation conditions under rice canopies. Crucially, the approach provides essential data for greenhouse gas inventories and supports the verification of low-emission water management practices, such as Alternate Wetting and Drying (AWD). Overall, the study demonstrated the value of multi-frequency SAR integration for advancing agricultural monitoring and climate-smart management in rice-growing regions. Full article

According to soil and atmospheric moisture conditions, drought can be categorized into soil drought, atmospheric drought, and compound drought. The effects of soil drought on evapotranspiration (ET) and canopy conductance (Gc) are extensively investigated in forests, but the responses of ET and Gc to atmospheric drought and compound drought still remain unclear in plantations. Environmental factors and ET were continuously measured in a Pinus sylvestris var. mongolica plantation located in the semi-arid areas of Northeast China during the growing seasons (May–September) in 2020–2024. Compared with non-drought, ET increased by 34.96% under atmospheric drought, and decreased by 23.58% and 28.86% under soil drought and combined drought, respectively. Compared with non-drought, Gc decreased by 29.27%, 15.19%, and 68.74% under atmospheric drought, soil drought, and combined drought, respectively. Different from other three water conditions, relative extractable water (REW) replaced net radiation as the most important influencing factor of ET under combined drought. And vapor pressure deficit (VPD) always had a relatively lower contribution to ET under the four water conditions. Unlike the other three water conditions, the dominant controlling factor of Gc was REW instead of VPD under combined drought. This study proved that different drought types have different effects on ET and Gc. Under warmer and drier climates, management practices such as pruning and thinning should be used to cope with the increasing water stress to ensure the sustainable development of the Pinus sylvestris var. mongolica plantations in the semi-arid areas of Northeast China. Full article

The forecast of fruit weight at harvest requires (i) a non-destructive method for assessment of weight ($F_w$) of fruit-on-tree and (ii) the knowledge of fruit growth dynamics. To address the first issue for mango fruit, several allometric relationships between $F_w$ and fruit-lineal dimensions of length ($L)$, width ($W)$, and thickness ($T)$ were considered, with the relationship $F_w=kLWT$ recommended. A $k$ value of 0.5146 was established for fruit of the cultivar Honey Gold for fruit past the stone-hardening stage, based on assessment of 1091 fruit from 13 season/orchard populations and preliminary values of 0.5376, 0.5151, and 0.5239 for the Keitt, Kensington Pride, and R2E2, respectively, on the basis of more limited datasets. A combined cultivar model was recommended across all cultivars considered, except Keitt. The variation in k between populations of Keitt and Honey Gold fruit was due to the difference in fruit density, rather than shape. This conclusion should be tested in context of other cultivars and fruit development. A correction for developmental age was established for Honey Gold fruit, viz., $k_{corr}=-0.0009\times DAFB+0.5975$ for fruit up to ~1224 growing degree days. The need for similar corrections for other cultivars should be investigated. For the second issue, the use of a linear function based on measurements of $F_w$ in the weeks immediately before harvest was recommended for forecasting harvest-time weight to an accuracy of approximately 10%. A Logistic function described $F_w$ increase better than a Gompertz function; however, a change in growing conditions during fruit development limits the reliability of such models for forecasting fruit weight at harvest maturity. Rather, it is proposed that a set of reference models based on a set of reference Logistic model parameters for a suite of growing conditions be developed for use in guiding agronomic interventions earlier in fruit development to maintain growth on a trajectory to achieve a desired weight at harvest. Full article

This study evaluated the effects of peat-based organomineral fertilizers with different compositions and Bacillus spp. inoculation on the growth and nutrient accumulation of loose-leaf lettuce grown under summer and winter conditions. Two independent greenhouse experiments were conducted using a randomized complete block design with eight treatments: no basal fertilized control (T1); conventional mineral fertilization (T2); peat-based organomineral fertilizers containing 50% (T3), 40% (T4), or 30% peat (T5); and the corresponding formulations supplemented with Bacillus subtilis, Bacillus megaterium, and Bacillus aryabhattai (T6–T8). All fertilized treatments were standardized to supply the same P rate. Multivariate analyses revealed a strong effect of fertilization strategy on plant growth and nutritional status. In both seasons, fertilized treatments significantly outperformed the control, while organomineral fertilizers performed similarly to or better than conventional mineral fertilization. The greatest shoot fresh mass and nutrient accumulation were observed in formulations containing lower peat proportions and higher mineral nutrient density, particularly when combined with Bacillus spp. inoculation. In the summer experiment, the 40% peat formulation supplemented with Bacillus spp. (T7) produced the highest shoot fresh mass (197.57 g plant⁻¹), whereas in the winter experiment the highest value was obtained with the 30% peat formulation supplemented with Bacillus spp. (T8; 157.86 g plant⁻¹). These treatments also exhibited greater accumulation of macronutrients and micronutrients, particularly N, P, K, Fe, Mn, and Zn. The results indicate that the performance of peat-based organomineral fertilizers was influenced by the balance between the organic matrix and mineral fraction, as well as by seasonal growing conditions. In addition, Bacillus spp. inoculation was associated with improved performance of formulations with greater mineral nutrient density but did not compensate for less favorable fertilizer compositions. Under the conditions evaluated, peat-based organomineral fertilizers containing lower peat proportions and supplemented with Bacillus spp. performed similarly to or better than conventional mineral fertilization and promoted greater lettuce growth and nutrient accumulation than the non-fertilized control. These findings are limited to a single lettuce cultivar grown in pots under greenhouse conditions across two seasonal experiments conducted at one location. Full article

Unmanned Aerial Vehicle (UAV)-based remote sensing using high-throughput spectral imaging has emerged as an effective non-destructive alternative for large-scale agricultural monitoring. This study evaluates the performance of UAV-based multispectral (MSI) and hyperspectral (HSI) imaging combined with machine learning for estimating in-season nitrogen uptake in spring wheat and canola. Field trials were conducted at irrigated and non-irrigated sites in southern and central Alberta, Canada, respectively, over three growing seasons (2023–2025). Coincident with ground-truth tissue sampling, aerial imagery was collected and processed to train and validate six machine learning models, using ~520 matchups per crop. All models successfully estimated nitrogen uptake across years and locations, although performance varied by sensor and data types. For canola, ANN produced the highest MSI-based accuracy (R² = 0.83, RMSE = 0.5%), whereas HSI data improved prediction performance, with SVR achieving the best results (R² = 0.90, RMSE = 0.40%). In wheat, ANN yielded the highest accuracy for both MSI and HSI data (R² = 0.77, RMSE = 0.54% for MSI; R² = 0.8, RMSE = 0.48% for HSI). These findings demonstrate that UAV-based spectral imaging combined with machine learning provides a reliable and scalable approach for non-destructive nitrogen uptake estimation. Although MSI sensors produced strong predictive performance, the enhanced spectral resolution of HSI data consistently improved estimation accuracy for both crops across varied growing conditions. Full article

Efficient nutrient management is essential for sustainable orchard production; however, conventional laboratory analyses used to assess plant nutritional status are time-consuming and expensive. Optical sensing technologies offer a rapid and non-destructive alternative. This study evaluated the potential of proximal optical sensors and UAV-based multispectral imagery to assess the nutritional status of young potted ‘Nashi’ pear (Pyrus pyrifolia (Burm. f.) Nakai) trees. Three fertilization treatments based on different concentrations of Hoagland solution were applied to 18 one-year-old potted trees. Leaf measurements were collected during the growing season using Dualex, CL-01 chlorophyll meter, and Pocket PEA fluorimeter, while UAV-based multispectral imagery was used to calculate vegetation indices, including NDVI, SR, OSAVI, and MSAVI. Leaf nitrogen (N), phosphorus (P), and potassium (K) concentrations were chemically determined and used as reference values for the regression analyses. Significant (p < 0.05) relationships were observed between leaf N content (N%) and several optical parameters related to leaf pigments, including chlorophyll, flavonols, and the Nitrogen Balance Index (NBI), as well as multispectral indices, although with weak associations (R² = 0.326–0.488). The strongest individual relationship with N% was shown by NBI (R² = 0.480). To account for repeated measurements on the same plants, linear mixed-effects models were fitted. These models indicated that NBI showed the strongest association with N% among the proximal optical parameters (β = 0.019, p < 0.001; RMSE = 0.113; MAE = 0.091), followed by flavonols and Dualex chlorophyll. In contrast, optical parameters showed limited sensitivity to P and K. Multispectral indices were not significantly related to K, while only Red and Green reflectance showed weak correlations with P. Overall, optical parameters showed the best associations with N% under the combined nutrient-gradient conditions tested, whereas the assessment of P and K remained limited and should be considered exploratory. Full article

We modelled, for the first time, the seasonal dynamics and long-term trends of Abies marocana forests (Rif Mountains, northern Morocco) using remote-sensing-derived vegetation indices. Using the MODIS Terra Vegetation Indices product MOD13Q1 (enhanced vegetation index, EVI; 16-day frequency; 250 m spatial resolution) from 2000 to 2024 (575 images over 25 years), we applied a robust seasonal trend analysis (RSTA) workflow, representing an inferential extension of classical seasonal trend analysis (STA) through the explicit control of Type I error under serial and spatial correlation. This approach combined: (i) harmonic regression to capture the annual and semi-annual cycles of A. marocana forests, estimating seasonal amplitudes and phases while filtering out low-frequency noise; (ii) an iterative trend-free prewhitening (TFPW) procedure following Wang and Swail, applied only to time series with significant serial autocorrelation according to the Durbin–Watson test; (iii) the Theil–Sen slope (TS) estimator, a robust non-parametric method, to quantify the magnitude and direction of seasonality trends; (iv) the contextual Mann–Kendall (CMK) test to assess the statistical significance of seasonality trends, while correcting for spatial autocorrelation and accounting for cross-correlation among neighbouring pixels; (v) the Benjamini–Hochberg (BH) procedure to control the false discovery rate (FDR), ensuring that only statistically robust seasonality trends were retained; and (vi) reconstruction of seasonal curves representing the beginning and end of the study period and derivation of phenological metrics from the statistically significant seasonal trends retained after inferential filtering. After applying the complete analytical workflow, statistically significant trends were detected in 79.2% of pixels within A. marocana forests, compared with 86.4% when prewhitening and false discovery rate control were not applied. All Theil–Sen slopes retained by the RSTA workflow were positive, with a mean slope of approximately 0.00175 EVI year⁻¹, corresponding to an average annual increase of roughly 0.7% and an overall increase of approximately 15% over the 2000–2024 study period relative to the initial mean EVI conditions. Browning trends identified by classical STA were not supported after inferential filtering and FDR control, indicating that all these patterns were spurious or only marginal, and confined to limited areas and edge zones. The reconstructed seasonal trend curves were consistent with a longer growing season, although this inference is based on land-surface vegetation dynamics rather than direct phenological observations. The long-term ecological consequences of these changes in seasonal vegetation activity will hinge on the interactions among warming, rising water demand, and potential disturbance regimes under future climatic conditions. Full article

Phosphorus (P) accumulation in intensively agricultural soils represents a growing environmental concern due to its potential mobilization and contribution to eutrophication. This study investigated the mechanisms controlling P availability and redistribution in agricultural soils from the Elota–Piaxtla Irrigation District (northwestern Mexico) during cropping and non-cropping periods. Soil P fractions were determined using the Hedley sequential extraction method and related to soil physicochemical properties through a correlation analysis. During the cropping period, P in Fe/Al hydroxides dominated (45–67% of total P), indicating strong adsorption and fixation in fine-textured soils. In contrast, the non-cropping period showed a significant increase in organic P in humic substances (up to 55%), suggesting enhanced biological transformation and residue recycling. Labile P fractions decreased from 60% to 44% of total P between sampling periods, while moderately labile fractions increased, indicating seasonal redistribution of P pools. Statistical analysis revealed that P dynamics were primarily governed by mineralogical characteristics and organic matter transformations rather than by individual soil properties. The accumulation of moderately labile and organic P fractions during fallow periods highlights a latent environmental risk, particularly in irrigated systems prone to runoff and erosion. These findings emphasize the need for fraction-based nutrient management strategies that integrate both agronomic efficiency and environmental protection in intensive agricultural soil. Full article

Rice–fish co-culture is widely promoted for mitigating nitrous oxide (N₂O) emissions from paddy soils, yet how the duration of co-culture reshapes the underlying nitrogen-cycling microbial community under low-nitrogen input remains poorly understood. This study aimed to (i) characterize how co-culture duration alters the rhizosphere microbial functional potential for N₂O production and consumption, and (ii) identify the water and soil variables linking fish activity to that response. The experiment was conducted during the 2024 rice growing season in the Qingtian rice–fish system (Zhejiang Province, China), a traditional agricultural heritage system managed without chemical fertilizer or supplementary feed. Three treatments (i.e., rice monoculture, first-year co-culture, and long-established (~10-year) co-culture) were compared using six independently bunded replicate plots each. Rhizosphere soils were collected at the tillering, heading and maturity stages for shotgun metagenomic profiling of nitrogen-cycling functional genes, with concurrent measurement of N₂O flux and water and soil physicochemical properties. Fluxes were uniformly low and did not differ among treatments (p > 0.05), defining a substrate-limited baseline. Against this baseline, first-year co-culture induced a coordinated shift toward complete denitrification (nosZ increased by 25–33% across all stages; nosZ/(nirK + nirS) rose to 0.99 at heading), associated with a transient water organic carbon pulse and dissolved-oxygen availability. The long-established system resembled monoculture, indicating a non-monotonic, duration-dependent response. Full article

Marine biological invasions are rapidly reshaping Mediterranean ecosystems, with growing consequences for biodiversity and fisheries. This study investigates recent changes in the composition of commercially important crustacean assemblages along the south-eastern coast of Sicily (central Mediterranean), focusing on penaeid shrimps (Penaeus aztecus and Penaeus kerathurus) and stomatopods (Erugosquilla massavensis and Squilla mantis). Field surveys were conducted during the fishing seasons of 2021 and 2025 at major landing sites and markets (Portopalo di Capo Passero, Syracuse and Catania), using standardized subsampling protocols applied to catches obtained by trammel nets and bottom trawls. Species composition was quantified through repeated sampling events, and temporal differences were analyzed using non-parametric tests and binomial generalized linear models, incorporating year and fishing gear as explanatory variables. Quantitative data were complemented by local ecological knowledge derived from structured interviews with professional fishers. Across the four-year interval, both taxonomic groups exhibited a pronounced shift in species dominance. The proportion of the invasive shrimp P. aztecus increased from approximately 20% in 2021 to over 80% in 2025, while the invasive stomatopod E. massavensis rose from about 2% to nearly 90% of total landings. These changes were statistically significant and independent of fishing gear. Fishers’ perceptions closely mirrored the quantitative trends, confirming the rapid replacement of native species by non-indigenous taxa and highlighting emerging socio-economic implications for local fisheries. Our findings document a rapid shift in the composition of commercial crustacean landings in Sicilian coastal waters, with invasive species becoming the dominant component of catches within a few years. This study underscores the need for adaptive fisheries management and integrated monitoring frameworks capable of responding to accelerating biological invasions in Mediterranean marine ecosystems. Full article

Eutrophication remains a persistent water-quality problem in shallow lakes, where external inputs interact with internal loading and biogeochemical cycling. Although floating treatment wetlands (FTWs) are increasingly promoted as nature-based solutions for water remediation, their field-scale interpretation in hydrologically complex eutrophic lakes remains challenging. This study examined the spatial organization of water quality during the operation of a floating-island system in a eutrophic urban lake affected by polluted tributary inflow. The study was not designed to quantify isolated FTW removal efficiency, but to evaluate spatial water quality organization during FTW operation under real-use field conditions. Water quality was monitored over two growing seasons across six functionally defined zones, and spatial and temporal patterns were analyzed using descriptive statistics and linear mixed-effects models. The results showed parameter-specific spatial structuring rather than a uniform treatment response. The clearest inlet-lake contrasts were observed for electrical conductivity (EC), suspended matter (SM), and nitrate nitrogen (NO₃-N), whereas biochemical oxygen demand (BOD₅), ammonium nitrogen (NH₄-N), and total organic carbon (TOC) showed lower values at the inlet and higher values in downstream zones. Dissolved oxygen (DO), oxygen saturation (SO), chemical oxygen demand (COD), nitrite nitrogen (NO_2-N), and orthophosphate phosphorus (PO₄-P) showed moderate or non-robust zonal effects. These findings indicate that FTWs in shallow eutrophic lakes should be evaluated through functional zoning and parameter-specific interpretation rather than as isolated units with uniform removal responses. Full article

Introduction: Biological invasions are a major driver of biodiversity loss, particularly in freshwater ecosystems. The Iberian Peninsula, a hotspot of endemic diversity, is increasingly threatened by invasive predatory fish, which may exert higher predatory rates under warmer environmental conditions, disrupting/endangering native fish communities. One such species is the European catfish (Silurus glanis), a large and voracious apex predator. Despite growing research, most telemetry studies have focused on lentic systems, limiting our understanding of its behaviour in lotic environments. Moreover, high-resolution biologging approaches remain largely unexplored. Objective: This study aims to characterize the habitat use and activity patterns of European catfish in a non-native lotic section of the lower Tagus River, and to identify key environmental drivers shaping its predatory behaviour. Methodology: Adult individuals were tagged with radio telemetry transmitters equipped with temperature, pressure (depth), and 3D-accelerometer archival sensors. A preliminary controlled experiment established activity thresholds to classify behaviours. Ten adult fish were then actively tracked over one year, combining spatial data with high-resolution biologging. Habitat use and activity patterns were analyzed across seasonal and circadian scales. Generalized Additive Models (GAMs) were used to assess the effects of environmental variables on activity levels and depth use, while Hurdle models were applied to identify the environmental drivers influencing the occurrence and frequency of burst activity events (predatory behaviour proxies). Results: Fish displayed strong site fidelity, frequently using structured habitats near riverbanks. European catfish also showed clear seasonal and circadian patterns in habitat use and activity, occupying deeper habitats in winter and shallower areas in warmer seasons. Activity occurred year-round, increasing in spring and summer and peaking at dusk, being influenced by temperature, river flow, season, and time of day. Burst activity occurred more often in spring and at dusk. Conclusions: This study unveils insights on European catfish behaviour in invaded lotic systems, highlighting consistent patterns linked to environmental conditions. These findings can support more targeted and effective management strategies for controlling this invasive species. Full article